Within data gathered through passive monitoring of patients with Multiple Sclerosis (MS), there is a clear necessity for improved methodological approaches to match the emergence of continuous, objective, measuring technologies. As most gold standards measure infrequently and require clinician presence, fluctuations in the daily progression are not accounted for. Due to the underlying conditions of homogeneity and stationarity (the main tenets of ergodicity) not being met for the majority of the statistical methods employed in the clinical setting, alternative approaches should be investigated. A solution is to use a non-linear time series analysis approach. Here, Early-Warning Signals (EWS) in the form of critical fluctuations in Keystroke Dynamics (KD), collected using participant’s smartphones, are investigated as indicators for a clinical change in three groups. These are patients with MS and changes in Magnetic Resonance Imaging (MRI), patients with MS but without changes in MRI, and healthy controls (HCs). Here, we report examples of EWS and changes in KD coinciding with clinically relevant changes in outcome measures in both patients with and without differences in the amount of MRI enhancing lesions. We also report no clinically relevant changes in EWS in the HC population. This study is a first promising step toward using EWS to identify periods of instability as measured by a continuous objective measure as a proxy for outcome measures in the field of MS.
Early-warning signals for disease activity in patients diagnosed with multiple sclerosis based on keystroke dynamics
Note: This paper is part of the Focus Issue on Dynamical Disease: A Translational Perspective.
J. Twose, G. Licitra, H. McConchie, K. H. Lam, J. Killestein; Early-warning signals for disease activity in patients diagnosed with multiple sclerosis based on keystroke dynamics. Chaos 1 November 2020; 30 (11): 113133. https://doi.org/10.1063/5.0022031
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